INVESTIGATION OF BUBBLE DYNAMICS AND HEATING DURING FOCUSED ULTRASOUND INSONATION IN TISSUE-MIMICKING MATERIALS

The deposition of ultrasonic energy in tissue can cause tissue damage due to local heating. For pressures above a critical threshold, cavitation will occur in tissue and bubbles will be created. These oscillating bubbles can induce a much larger thermal energy deposition in the local region. Traditionally, clinicians and researchers have not exploited this bubble-enhanced heating since cavitation behavior is erratic and very difficult to control. The present work is an attempt to control and utilize this bubble-enhanced heating. First, by applying appropriate bubble dynamic models, limits on the asymptotic bubble size distribution are obtained for different driving pressures at 1 MHz. The size distributions are bounded by two thresholds: the bubble shape instability threshold and the rectified diffusion threshold. The growth rate of bubbles in this region is also given, and the resulting time evolution of the heating in a given insonation scenario is modeled. In addition, some experimental results have been obtained to investigate the bubble-enhanced heating in an agar and graphite based tissue- mimicking material. Heating as a function of dissolved gas concentrations in the tissue phantom is investigated. Bubble-based contrast agents are introduced to investigate the effect on the bubble-enhanced heating, and to control the initial bubble size distribution. The mechanisms of cavitation-related bubble heating are investigated, and a heating model is established using our understanding of the bubble dynamics. By fitting appropriate bubble densities in the ultrasound field, the peak temperature changes are simulated. The results for required bubble density are given. Finally, a simple bubbly liquid model is presented to estimate the shielding effects which may be important even for low void fraction during high intensity focused ultrasound (HIFU) treatment.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.